Apparatus and method for identifying possible defect indicators for a valve

ABSTRACT

A method, apparatus, and computer program are provided for identifying a defective valve. The method, apparatus, and computer program identify one or more indicators of a possible defect in the valve at a plurality of resolution levels. The one or more indicators are identified using at least one of one or more operating characteristics associated with the valve. The method, apparatus, and computer program generate a plurality of indexes associated with the resolution levels, where the indexes are based on the one or more indicators and identify a likelihood of a valve defect. The method, apparatus, and computer program select one of the plurality of resolution levels using at least one of the indexes. The method, apparatus, and computer program determine an overall probability of a valve defect using at least one index associated with the selected resolution level.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application is related to U.S. patent application Ser. No.10/717,406 entitled “APPARATUS AND METHOD FOR IDENTIFYING POSSIBLEDEFECT INDICATORS FOR A VALVE” filed on Nov. 19, 2003, which isincorporated by reference.

TECHNICAL FIELD

This disclosure relates generally to process control systems and morespecifically to an apparatus and method for identifying defectivevalves.

BACKGROUND

Processing facilities are typically managed using process controlsystems. Among other functions, these control systems often manage theuse of valves in the processing facilities. The valves typically controlthe flow of materials in the facilities. Example processing facilitiesinclude manufacturing plants, chemical plants, crude oil refineries, andore processing plants. In these facilities, the valves may control theflow of water, oil, hydrochloric acid, or any other or additionalmaterials in the facilities.

The valves used in the processing facilities often suffer from a numberof problems or defects. For example, a valve may suffer from valvehysteresis or valve stiction. Valve hysteresis occurs when the valve ismoving in one direction, the control system instructs the valve to movein the opposite direction by a specified amount, and the valve moves inthe opposite direction by less than the specified amount. Valvestiction, which is short for static friction, refers to the resistanceto the start of motion. It occurs when the valve fails to respond topressure meant to adjust the opening of the valve. The valve fails torespond until additional pressure is added, which causes the valve toopen or close more than desired. These or other defects often limit orprevent the control systems from accurately controlling the flow ofmaterials using the valve.

SUMMARY

This disclosure provides an apparatus and method for identifyingdefective valves.

In one aspect, a method, apparatus, and computer program identify one ormore indicators of a possible defect in the valve at a plurality ofresolution levels. The one or more indicators are identified using atleast one of one or more operating characteristics associated with thevalve. The method, apparatus, and computer program generate a pluralityof indexes associated with the resolution levels, where the indexes arebased on the one or more indicators and identify a likelihood of a valvedefect. The method, apparatus, and computer program select one of theplurality of resolution levels using at least one of the indexes. Themethod, apparatus, and computer program determine an overall probabilityof a valve defect using at least one index associated with the selectedresolution level.

In particular aspects, the one or more operating characteristics includeat least one of measurements of a process variable associated with theoperation of the valve and values generated to control the operation ofthe valve. The one or more indicators include at least one of jumps inthe process variable measurements and extreme positions in the generatedcontrol values.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following description, taken in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example system for identifying a defective valveaccording to one embodiment of this disclosure;

FIG. 2 illustrates example variables used to identify a defective valveaccording to one embodiment of this disclosure;

FIGS. 3A and 3B illustrate example indications of a defective valveaccording to one embodiment of this disclosure;

FIG. 4 illustrates additional example indications of a defective valveaccording to one embodiment of this disclosure;

FIG. 5 illustrates example stiction patterns according to one embodimentof this disclosure;

FIGS. 6A through 6C illustrate example probability classificationsindicating whether valves are defective according to one embodiment ofthis disclosure;

FIG. 7 illustrates an example method for identifying a defective valveaccording to one embodiment of this disclosure; and

FIG. 8 illustrates an example method for identifying indications of adefective valve according to one embodiment of this disclosure.

DETAILED DESCRIPTION

FIG. 1 illustrates an example system 100 for identifying a defectivevalve according to one embodiment of this disclosure. The system 100shown in FIG. 1 is for illustration only. Other systems may be usedwithout departing from the scope of this disclosure.

In the illustrated example, one or more materials flow through a pipe102, and the flow of materials through the pipe 102 is controlled by avalve 104. The pipe 102 represents any suitable structure capable offacilitating the transport of one or more materials. The pipe 102 could,for example, represent a steel or plastic pipe or tube capable offacilitating the transport of oil, water, hydrochloric acid, or anyother material or materials.

The valve 104 controls the rate at which the material or materials flowthrough the pipe 102. The valve 104 may, for example, change an openingin the pipe 102, where a larger valve opening allows more material toflow through the pipe 102. The valve 104 includes any structure capableof controlling the flow of one or more materials through a pipe 102.

In the illustrated example, the system 100 includes a measuring device106, a controller 108, and a valve adjuster 110. The measuring device106 monitors one or more characteristics associated with the material(s)flowing through the pipe 102. For example, the measuring device 106 maymeasure the flow rate of a material flowing through the pipe 102. Themeasuring device 106 could monitor any other or additionalcharacteristics of the material flowing through the pipe 102. Themeasuring device 106 also outputs a signal 112 to the controller 108,where the signal 112 includes values identifying the measurements madeby the measuring device 106. The flow rate or other monitoredcharacteristic may be referred to as a process variable, and the signal112 provided to the controller 108 may be referred to as a processvariable (PV) signal. The measuring device 106 includes any hardware,software, firmware, or combination thereof capable of measuring at leastone characteristic of the material flowing through the pipe 102.

The controller 108 controls the opening and the closing of the valve 104in the system 100. In this example embodiment, the controller 108 usesthe process variable signal 112 provided by the measuring device 106 anda set point (SP) 114 to control the valve 104. The set point 114identifies the desired value for the process variable signal 112. Forexample, the controller 108 may adjust the valve opening so that theflow rate through the pipe 102 remains at or near a level indicated bythe set point 114. Using the process variable signal 112 and the setpoint 114, the controller 108 generates an output signal (OP) 116containing output values. The output values indicate the extent to whichthe valve 104 should be opened. The controller 108 includes anyhardware, software, firmware, or combination thereof for controlling theoperation of the valve 104. As a particular example, the controller 108could include one or more software routines stored in at least onememory and executed by at least one processor. Additional details of howthe controller 108 controls the valve 104 are provided below.

The valve adjuster 110 uses the values in the output signal 116 toadjust the valve opening or allow the valve 104 to remain in its currentposition. For example, in some embodiments, the output values in thesignal 116 identify the relative amount of change that is needed for thevalve 104. In these embodiments, positive values could indicate that thevalve 104 should be opened more, negative values could indicate that thevalve 104 should be closed more, and zero could indicate that no changeis needed. In other embodiments, the output values in the signal 116identify the absolute position of the valve 104. In these embodiments, aminimum value such as zero could indicate that the valve 104 should becompletely closed, a maximum value could indicate that the valve 104should be completely opened, and a value in between indicates that thevalve 104 should be partially opened. The valve adjuster 110 includesany structure capable of opening and/or closing a valve 104.

As described in more detail below, the process variable signal 112and/or the output signal 116 is used to identify a possible defect inthe valve 104. The ability to identify a possible defect in the valve104 may be implemented within the controller 108 or within a defectdetector 118 that resides external to the controller 108. The defectdetector 118 includes any hardware, software, firmware, or combinationthereof that is capable of identifying possible defects in a valve 104.As a particular example, the defect detector 118 could include one ormore software routines stored in at least one memory and executed by atleast one processor. The description that follows often describes thecontroller 108 processing information and identifying a possible defectin a valve 104. The same or similar functionality could also beimplemented in the defect detector 118.

The process variable signal 112 and/or the output signal 116 may be usedto identify one type or different types of defects in the valve 104. Forexample, the controller 108 could identify when the valve 104 issuffering from valve hysteresis or valve stiction. Valve hysteresisoccurs when the valve 104 is moving in one direction (opening orclosing), the controller 108 instructs the valve 104 to move in theopposite direction by a specified amount, and the valve 104 moves in theopposite direction by less than the specified amount. Static friction or“stiction” occurs when the valve 104 fails to respond to pressure fromthe valve adjuster 110 until additional pressure is applied to the valve104. At that point, the valve 104 jumps to a larger or smaller openingthan desired. These or other defects limit or prevent the controller 108from accurately controlling the valve 104.

The following description often describes the use of the system 100 indetecting the presence of stiction in the valve 104. This is for ease ofillustration and explanation only. The system 100 could use the same orsimilar techniques to identify other defects in a valve 104. Forexample, the characteristics of the process variable signal 112 and theoutput signal 116 could be the same or similar whether the valve 104 issuffering from valve hysteresis or valve stiction. As a result, thesystem 100 could also be used to identify other defects in the valve104.

Although FIG. 1 illustrates one example of a system 100 for identifyinga defective valve 104, various changes may be made to FIG. 1. Forexample, the controller 108 could control the operation of any number ofvalves 104. Also, the valve 104 and the valve adjuster 110 could form asingle integral unit.

FIG. 2 illustrates example variables used to identify a defective valve104 according to one embodiment of this disclosure. In particular, FIG.2 illustrates time series of the process variable signal 112 and theassociated output signal 116. The time series are collected at asampling rate of one sample per second, and five thousand data samplesare collected for each variable. The time series of the process variablesignal 112 and the output signal 116 shown in FIG. 2 are forillustration only. The process variable signal 112 and the output signal116 could have any suitable values depending on the system 100 in whichthe controller 108 or defect detector 118 operates.

As shown in FIG. 2, the process variable signal 112 oscillates betweentime periods 202 having higher measurement values and time periods 204having lower measurement values. This indicates that the values in thesignal 112 are oscillating around the set point 114 used by thecontroller 108. Also, as shown in FIG. 2, the values in the outputsignal 116 generally decrease during time periods 202 and generallyincrease during time periods 204. This indicates that the values in theoutput signal 116 constantly wander between high and low peak values.

As described below, the process variable signal 112 and the outputsignal 116 are used to identify a valve 104 that may be or that isdefective. For example, the behavior of the process variable signal 112and the output signal 116 shown in FIG. 2 may indicate that the valve104 is suffering from stiction or other defect.

Stiction often involves two phases, a “stick” phase and a “slip” phase.The stick phase occurs when the valve adjuster 110 applies force to thevalve 104 and the valve 104 sticks or does not move (open or close).This causes the controller 108 to instruct the valve adjuster 110 toapply additional force to the valve 104. The slip phase occurs when thevalve 104 finally slips or moves, but the additional force applied bythe valve adjuster 110 causes the valve 104 to open or close more thandesired.

In FIG. 2, multiple lines 206 indicate times when the valve 104 slips.In other words, the lines 206 identify when the opening in the valve 104changes more than desired. In general, the area around a line 206represents the slip phase of the valve 104, and the area between twolines 206 represents the stick phase of the valve 104.

The presence of stiction may explain the behavior of the processvariable signal 112 and the output signal 116. When in the stick phase,the process variable signal 112 seems to remain centered at a relativelyconstant value, and the output signal 116 is increasing or decreasing.At this point, the process variable signal 112 indicates, for example,that the flow rate of material through the pipe 102 is remainingrelatively constant. The increasing or decreasing output signal 116indicates that the controller 108 is instructing the valve adjuster 110to apply more and more force to the valve 104.

When in the slip phase, the process variable signal 112 jumps (lowervalues to higher values or vice versa), and the output signal 116switches direction (increasing to decreasing or vice versa). The jump inthe process variable signal 112 indicates, for example, that the flowrate of material through the pipe 102 has changed by a relatively largeamount. This may be caused by the application of enough force toovercome valve stiction, but the amount of force was excessive andcaused the valve 104 to open or close more than desired. The change indirection of the output signal 116 indicates that the controller 108 hasbegun instructing the valve adjuster 110 to change the valve opening inthe other direction to compensate for the larger than expected change inthe flow rate.

As a particular example, during a time period 202 when the processvariable signal 112 is higher, this may indicate that the flow ratethrough the pipe 102 is too high. The controller 108 attempts todecrease the valve opening to reduce the flow rate, but stiction causesthe valve 104 to retain its current amount of opening. This causes thecontroller 108 to instruct the valve adjuster 110 to apply more and moreforce to the valve 104, which is indicated by the falling output signal116. Eventually, enough force is applied to the valve 104 and the valveopening changes, but the excessive force causes the valve opening todecrease more than desired. This leads to the following time period 204where the process variable signal 112 is lower, which may indicate thatthe flow rate through the pipe 102 is now too low. This causes thecontroller 108 to instruct the valve adjuster 110 to increase the valveopening. Stiction causes the valve 104 to retain its current amount ofopening, which causes the controller 108 to instruct the valve adjuster110 to apply more and more force to the valve 104. This is indicated bythe rising output signal 116.

Although FIG. 2 illustrates one example of the variables used toidentify a defective valve 104, various changes may be made to FIG. 2.For example, any suitable values may form the process variable signal112 and the output signal 116. Also, the variables could be sampled atany suitable sampling rate that can reveal valve stiction using thesampled data. In addition, additional variables could be used to controlthe valve 104 and/or to identify a defective valve 104.

FIGS. 3A and 3B illustrate example indications of a defective valve 104according to one embodiment of this disclosure. In particular, FIGS. 3Aand 3B illustrate an example wavelet decomposition used to identifyjumps in the process variable signal 112. The indications shown in FIGS.3A and 3B are for illustration only. Other indications of a defectivevalve 104 may be used without departing from the scope of thisdisclosure.

As shown in FIG. 3A, the process variable signal 112 is received by thecontroller 108. The controller 108 performs wavelet decomposition togenerate wavelet coefficients at multiple resolution levels 302 a-302 g,plus low frequency content at level 302 h. Each of the resolution levels302 a-302 g represents different information of the process variablesignal 112. In particular, each of the levels 302 a-302 g representschanges in the process variable signal 112. The higher levels (startingat level 302 a) represent higher-frequency changes in the processvariable signal 112, and the lower levels (starting at level 302 g)represent lower-frequency changes in the process variable signal 112.

The controller 108 uses any suitable technique to generate the redundantwavelet coefficients in the resolution levels 302 a-302 g. In someembodiments, the controller 108 uses a Discrete Dyadic Wavelet Transform(DDWT) to generate the wavelet coefficients. While FIG. 3A illustratesthe generation of wavelet coefficients at seven different resolutionlevels 302 a-302 g, the controller 108 could generate waveletcoefficients at any number of levels.

The wavelet coefficients in the different resolution levels 302 a-302 gare related. The controller 108 uses these relationships to identifypossible jumps in the process variable signal 112. As shown in FIG. 3B,the controller 108 groups the wavelet coefficients at differentresolution levels 302 a-302 g into groups 304 a-304 e. In this example,each of the groups 304 a-304 e includes three adjacent resolution levelsof wavelet coefficients. In other embodiments, the controller 108 couldgroup any number of resolution levels into any number of groups.

Using the groups 304 a-304 e, the controller 108 identifies possibleprocess variable jumps 306 at different resolution levels 308 a-308 e.Each jump 306 represents a possible location where the process variablesignal 112 changes or jumps by a relatively large amount. As a result,each jump 306 represents a possible occurrence of the slip phase of astiction event.

The controller 108 uses any suitable technique to identify the possiblejumps 306 at the resolution levels 308 a-308 e. In some embodiments, thecontroller 108 uses singularity detection on the groups 304 a-304 e toidentify the jumps 306 at the resolution levels 308 a-308 e.

As shown in FIG. 3B, the higher resolution levels (starting at level 308a) identify more jumps 306 than the lower resolution levels (starting atlevel 308 e). Because the resolution levels 308 a-308 e identifydifferent numbers of process variable jumps 306, each of the resolutionlevels 308 a-308 e could indicate different amounts of stictionoccurring in the valve 104.

In this example, there are many false jumps 306 identified in the higherresolution levels (starting at level 308 a) due to significanthigh-frequency noise contained in the example signal 112. In this case,the jumps 306 identified at lowest resolution level 308 e are mostaccurate. However, the most accurate resolution level may not always bethe lowest resolution level 308 e. This is because different processvariable signals 112 could have different noise, drift, and oscillationbehavior. It is also possible that the stiction experienced by a valve104 is constant or intermittent. These factors may cause different onesof the resolution levels 308 a-308 e to more accurately represent thepattern of stiction in the valve 104. As a particular example, some ofthe higher resolution levels may include false jumps 306, and some ofthe lower resolution levels may lose jumps 306 due to smoothing effects.One technique for selecting the most accurate of the resolution levels308 a-308 e is described below.

Although FIGS. 3A and 3B illustrate one example of the indications of adefective valve 104, various changes may be made to FIGS. 3A and 3B. Forexample, wavelet coefficients at any number of resolution levels 302a-302 g could be produced. Also, any number of groups 304 a-304 e couldinclude any number of resolution levels 302 a-302 g. In addition, jumps306 in any number of resolution levels 308 a-308 e could be identified.

FIG. 4 illustrates additional example indications of a defective valve104 according to one embodiment of this disclosure. In particular, FIG.4 illustrates how extreme positions in the output signal 116 produced bythe controller 108 are identified. The example indications shown in FIG.4 are for illustration only. Other indications of a defective valve 104may be used without departing from the scope of this disclosure.

As shown in FIG. 4, the output signal 116 constantly wanders betweenhigh and low peaks. The high and low peaks may be referred tocollectively as extreme positions. The presence of an extreme positionin the output signal 116 may indicate the occurrence of the slip phaseof a stiction event.

The controller 108 may process the original output signal 116 or afiltered or “denoised” version 402 of the output signal. The controller108 uses any suitable technique to identify the extreme positions in theoutput signal 116. For example, the controller 108 could classify aparticular position in the output signal 116 as an extreme position ifit satisfies either of the conditions:OP(position)≧max(OP(position±range))  (1)OROP(position)≦min(OP(position±range))  (2)where OP(position) refers to the particular position in the outputsignal 116, and range defines an area around the particular position. Inthese embodiments, the particular position represents an extremeposition if it has a value greater than or less than all other valuesfalling within the range around that particular position.

The controller 108 could use any suitable technique for identifying therange used to identify extreme positions. In some embodiments, thecontroller 108 identifies the locations of extreme positions 404 atdifferent resolution levels 406 a-406 e. In these embodiments, thecontroller 108 identifies a range value for each of the resolutionlevels 406 a-406 e. In particular embodiments, the controller 108identifies the range values for the resolution levels 406 a-406 e usingthe formula:

$\begin{matrix}{{Range}_{k} = \frac{TotalDataLength}{{PV}\mspace{14mu}{Jumps}_{k}}} & (3)\end{matrix}$where Range_(k) represents the range value corresponding to the kthresolution level 406 a-406 e, TotalDataLength represents the totalnumber of samples in the output signal 116, and PVJumps_(k) representsthe total number of process variable jumps 306 detected in the kthresolution level 308 a-308 e shown in FIG. 3B.

The extreme positions 404 may be identified at multiple resolutionlevels 406 a-406 e. As described above, the most accurate level may notalways be the lowest resolution level 406 e. One technique for selectingthe most accurate of the resolution levels 406 a-406 e is describedbelow.

Although FIG. 4 illustrates an additional example of indications of adefective valve 104, various changes may be made to FIG. 4. For example,the extreme positions 404 may be detected at any number of resolutionlevels using corresponding PVJumps_(k) detected at those resolutionlevels. Also, the extreme positions 404 may be detected using theoriginal output signal 116 or the filtered output signal 402.

FIG. 5 illustrates example stiction patterns according to one embodimentof this disclosure. The patterns shown in FIG. 5 are for illustrationonly. Other patterns may be generated and processed without departingfrom the scope of this disclosure.

As described above, process variable jumps 306 in the process variablesignal 112 and/or extreme positions 404 in the output signal 116 may bedetected at one or multiple resolution levels. Using the jumps 306and/or the extreme positions 404, the controller 108 identifies thepattern of stiction for each of the resolution levels. In someembodiments, a stiction pattern includes a slip phase 502 and apreceding stick phase 504. The controller 108 uses the jumps 306 and/orthe extreme positions 404 as reference points to identify the slipphases 502 and the stick phases 504. A slip phase 502 and the precedingstick phase 504 collectively form a stiction pattern 506.

The controller 108 uses any suitable technique for identifying thestiction patterns 506. In some embodiments and as shown in FIG. 5, thecontroller 108 identifies a process variable jump 306 and local minimumand local maximum values around the jump 306. The controller 108 alsocalculates a “process variable change” as the local maximum value minusthe local minimum value. The controller 108 further identifies a slipphase 502 as the smallest region around the jump 306 that includes aspecified percentage of the process variable change, such as eighty fivepercent. In addition, the controller 108 identifies a stick phase 504 asthe largest region preceding the slip phase 502 that occupies less thana specified percentage of the process variable change, such as twentypercent. This produces one stiction pattern 506.

In other embodiments, the controller 108 identifies the average lengthof the stiction patterns 506 using the formula:

$\begin{matrix}{{AverageLength} = \frac{TotalDataLength}{NumberOPExtremes}} & (4)\end{matrix}$where AverageLength represents the average length of the stictionpatterns 506, TotalDataLength represents the total number of samples inthe output signal 116, and NumberOPExtremes represents the number ofextreme positions 404 in the output signal 116. For each extremeposition 404, the controller 108 then defines the stick phase 504 as theregion preceding an extreme position 404 having a specified portion ofthe average length, such as seventy five percent of the average length.The controller 108 further defines the slip phase 502 as the regionafter an extreme position 404 having a specified portion of the averagelength, such as twenty five percent of the average length. This producesone stiction pattern 506.

When the process variable jumps 306 and the output signal extremepositions 404 are produced at multiple resolution levels, the controller108 could identify the stiction patterns 506 for each resolution level.Also, as described below, the stiction patterns 506 are used tocalculate one or more indexes, which are used to identify theprobability that a valve 104 is suffering from stiction or other defect.

Although FIG. 5 illustrates one example of stiction patterns 506,various changes may be made to FIG. 5. For example, the stictionpatterns 506 may be produced in any suitable manner.

FIGS. 6A through 6C illustrate example probability classificationsindicating whether valves 104 are defective according to one embodimentof this disclosure. The probability classifications shown in FIGS. 6Athrough 6C are for illustration only. Other probability classificationscould be used without departing from the scope of this disclosure.

As described above, the controller 108 uses the process variable jumps306, the output signal extreme positions 404, or both to generatestiction patterns 506. FIG. 6A illustrates the probabilities that valves104 are defective based on the process variable jumps 306, and FIG. 6Billustrates the probabilities that valves 104 are defective based on thecontroller output signal extreme positions 404. FIG. 6C shows theprobability classifications produced using both the process variablejumps 306 and the output signal extreme positions 404.

The controller 108 uses the stiction patterns 506 to calculate one ormore indexes, which are used to identify the probability that a valve104 is suffering from stiction or other defect. The controller 108 maygenerate any suitable index or indexes. The following are exampleindexes that could be calculated, although any other or additionalindexes could be produced and used by the controller 108.

A stick/slip PV movement ratio represents the average absolute PVmovements in the stick phases 504 divided by the average absolute PVmovements in the slip phases 502. When valve stiction occurs, the PVmovement is typically larger in the slip phases 502 and smaller in thestick phases 504. As a result, a smaller stick/slip PV movement ratioindicates a higher probability that the valve 104 is suffering from adefect.

A slip/stick duration ratio represents the average duration of the slipphases 502 divided by the average duration on the stick phases 504. Whenvalve stiction occurs, the stick phases 504 are often longer than theslip phases 502. As a result, a smaller slip/stick duration ratioindicates a higher probability that the valve 104 is suffering from adefect.

A stick/slip PV/OP gain ratio represents the ratio of the “stick phasegain” to the “slip phase gain.” The stick phase gain represents theaverage of the absolute PV movements divided by the average of theabsolute OP movements in the stick phases 504. The slip phase gainrepresents the average of the absolute PV movements divided by theaverage of the absolute OP movements in the slip phases 502. When valvestiction occurs, the stick phases 504 often include lesser PV movementsand larger and consistent OP movements, so the stick phase gain shouldbe smaller. The slip phases 502 often include larger sudden PVmovements, so the slip phase gain should be larger. Smaller PV/OP gainratios indicate higher probabilities of a valve defect.

A number of stiction features value represents the total number ofstiction patterns 506 identified. A larger number of stiction featureswithin a given amount of time often indicates a higher likelihood of avalve defect. A standard deviation of OP movements in the stick phases504 could also be used. When valve stiction occurs, the OP movements inthe stick phases 504 may be relatively consistent, so smaller standarddeviations may indicate higher probabilities that a valve 104 isdefective.

A stiction feature ratio represents the duration of the total stictionpatterns 506 divided by the total duration of the collected data. Forexample, the controller 108 may be unable to determine whetherparticular portions of the collected data represent a stiction pattern506. As a particular example, the controller 108 could determine thatthe stiction patterns 506 occupy seventy percent of the total duration,so the stiction feature ratio is 0.7. Larger stiction feature ratios mayindicate higher probabilities of a valve defect.

A standard deviation of the process gain could be calculated. In eachstiction pattern 506, the process gain represents the PV movement in theslip phase 502 divided by the OP movement in stick phase 504. When valvestiction occurs, the process gains calculated from the extractedstiction patterns 506 may be relatively consistent. A smaller standarddeviation in the calculated process gains therefore represents a largerlikelihood of a valve defect.

A PV jump/OP extreme position ratio represents the number of PV jumps306 divided by the number of OP extreme positions 404. The likelihood ofa valve defect increases as the PV jump/OP extreme position ratioapproaches a value of one.

A PV jump and OP extreme position consistency value represents theaverage difference in time between the detected OP extreme positions 404and the detected PV jumps 306, divided by the average duration of thestiction patterns 506. Larger probabilities of a valve defect maycorrespond to smaller consistency values.

These indexes may be weighted differently and used to produce an overallprobability that a defect exists. For example, the stick/slip PVmovement ratio may be more useful in identifying stiction than thestiction feature ratio, so the PV jump/OP extreme position ratio isgiven a higher weight.

In particular embodiments, the PV jumps 306 and the OP extreme positions404 are identified at multiple resolution levels. In these embodiments,one or more indexes may be generated at each resolution level. Theprobability of a defect can be calculated as a function of the indexes,such as using the formula:P(k)=F(Index(k))  (5)where P(k) represents the probability associated with the kth resolutionlevel and Index(k) represents the values of the indexes produced for thekth resolution level. The function of the indexes could represent any ofa wide variety of standard or proprietary functions.

In particular embodiments, the probability of a defect may be calculatedas a weighted sum of the indexes using the formula:P(k)=(Index(k))^(T) *W+b  (6)where Index(k) represents an array of the indexes for the kth resolutionlevel, (Index(k))^(T) represents the transpose of the array, Wrepresents an array of weights for the indexes, and b represents a biasterm. To ensure that the probability has a value between zero and oneinclusive, the probability could be determined using the formula:

$\begin{matrix}{{P(k)} = {\frac{1}{\left( {1 + \exp^{- {({{{({{Index}{(k)}})}^{T}*W} + b})}}} \right)}.}} & (7)\end{matrix}$Depending on the formula used to determine the probability, values forthe weights W may be selected in any suitable manner, such as byspecifying the weights by experience or performing regression tocalculate the weights.

To make the results more accurate, a nonlinear relationship of theindexes and the probability may be included in the probabilitycalculation. For example, if there is only one stiction pattern 506detected over a long time period, the probability should be small nomatter how favorable other indexes are for producing a high stictionprobability. This is due to the fact that the pattern did not repeat.This or other examples may be freely incorporated as nonlinearrelationships between the probability and one or more of the indexes.

The probabilities calculated using one or more of Equations (5)-(7) areused to select the best resolution level. The best resolution levelrepresents one of the resolution levels 308 a-308 e and/or one of theresolution levels 406 a-406 e, and the selected resolution level is usedto identify the overall probability that a valve 104 is defective.

As described above, the controller 108 uses any suitable technique foridentifying the stiction patterns 506. One technique involves using theprocess variable jumps 306, and another technique involves using theoutput signal extreme positions 404. In particular embodiments, thecontroller 108 uses one or both techniques to generate one or multiplesets of stiction patterns 506. When both techniques are used to generatemultiple sets of stiction patterns 506, the overall probability of adefect may be calculated using the formulas:Pr _(PV)=Max(F _(PV)(index_(PV)(level)))  (8)Pr _(OP)=Max(F _(OP)(index_(OP)(level)))  (9)Overall=F(PR _(PV) , PR _(OP)).  (10)where:

Pr_(PV) represents the probability of stiction calculated using thestiction patterns 506 produced with the process variable jumps 306 (suchas those stiction patterns 506 produced using the technique described inParagraph [053]);

Pr_(OP) represents the probability of stiction calculated using thestiction patterns 506 produced with the extreme positions 404 (such asthose stiction patterns 506 produced using the technique described inParagraph [054]);

index_(PV) represents the indexes calculated using the stiction patterns506 produced with the process variable jumps 306;

index_(OP) represents the indexes calculated using the stiction patterns506 produced with the extreme positions 404;

level represents the resolution level at which the process variablejumps 306 or the OP extreme positions 408 are identified;

F_(PV) and F_(OP) represent the functions used to calculate theprobability from the indexes as described above; and

Overall represents the overall probability that a valve 104 isdefective.

In this example, the two valve stiction probabilities are calculated byusing the process variable jumps 306 and the OP extreme positions 404 atthe resolution level that has the larger probability value. The overallprobability of a valve defect is then calculated as a function of thetwo stiction probabilities.

In particular embodiments, the overall probability Overall is calculatedusing the following conditions (where | means or, & means and, y1represents Pr_(PV), y2 represents Pr_(OP), and y represents Overall):

if (y1 >=0.7 | y2 >=0.7) then   y=max(y1,y2); elseif (y1 >=0.6 & y2 >=0.6) then   y= 0.7; elseif (y1 >= 0.6 & y2 > 0.2) then   y=max(y1,y2);elseif (y2 >=0.6 & y1 > 0.2) then   y=max(y1,y2); elseif (y1 >=0.5 |y2 >=0.5) then   y=min(0.6,((y1−0.2)/0.8+(y2−      0.2)/0.8)); elseif(y1 <=0.2 | y2 <=0.2) then   y=0; else   y=min(0.6,(min(0.5,max(0,(y1−     0.2)/0.8))+min(0.5,max(0,(y2−      0.2)/0.8)))); endThis represents one possible technique for identifying the overallprobability of a valve defect. Other techniques could also be used.

In other embodiments, the controller 108 may use either the processvariable jumps 306 or the output signal extreme positions 404 togenerate a single set of stiction patterns 506 on each resolution level.In these embodiments, the overall probability of a valve defect may becalculated using either Equation (8) or Equation (9).

FIG. 6A illustrates the overall probabilities of a valve defectcalculated using Equation (8) (combined with Equation (7) and nonlinearprocessing of the indexes as described above). The PV signal 112 and theOP signal 116 used in this example were collected from various realoperational processes. Over two thousand datasets were collected thatare associated with more than one thousand valves 104. Approximatelyfive hundred datasets are associated with defective valves 104, andapproximately seventeen hundred datasets are associated withnon-defective valves 104. The datasets are all collected at a samplingrate of one sample per second, and each dataset contains one hour ofdata for the PV signal 112 and OP signal 116.

In this example, FIG. 6A is divided into a first portion 602 and asecond portion 604. The first portion 602 identifies the probabilitiescalculated for valves 104 that suffer from a defect, and the secondportion 604 identifies the probabilities calculated for valves 104 thatdo not suffer from a defect.

In FIG. 6A, the calculated probabilities are divided into threeclassifications 606-610. The class 606 represents lower probabilities ofa defect, the class 608 represents intermediate probabilities of adefect, and the class 610 represents higher probabilities of a defect.As shown in FIG. 6A, a majority of the probabilities for the defectivevalves 104 in the first portion 602 fall within the highest class 610,and almost all of the probabilities fall within the higher two classes608-610. Similarly, many of the probabilities for the non-defectivevalves 104 in the second portion 604 fall within the lowest class 606,and almost all of the probabilities fall within the lower two classes606-608.

FIG. 6B illustrates the overall probabilities of a valve defectcalculated using Equation (9) (combined with Equation (7) and nonlinearprocessing of the indexes as described above). The datasets are the sameas those used in FIG. 6A.

In this example, FIG. 6B is divided into a first portion 652 and asecond portion 654. The first portion 652 identifies the probabilitiescalculated for valves 104 that suffer from a defect, and the secondportion 654 identifies the probabilities calculated for valves 104 thatdo not suffer from a defect. The probabilities are also divided intothree classifications 656-660. The class 656 represents lowerprobabilities of a defect, the class 658 represents intermediateprobabilities of a defect, and the class 660 represents higherprobabilities of a defect.

As shown in FIG. 6B, a majority of the probabilities for the defectivevalves 104 in the first portion 652 fall within the highest class 660,and almost all of the probabilities fall within the higher two classes658-660. Similarly, many of the probabilities for the non-defectivevalves 104 in the second portion 654 fall within the lowest class 656,and almost all of the probabilities fall within the lower two classes656-658.

As shown in FIGS. 6A and 6B, using only the process variable jumps 308or only the output signal extreme positions 404, the controller 108classifies the probabilities of a defect into the extreme classes(highest for defective valves 104 and lowest for non-defective valves104) correctly for many of the valves 104. Also, the controller 108rarely classifies the probability of stiction into the wrong extremeclass (lowest for defective valves 104 and highest for non-defectivevalves 104).

For probabilities falling into the intermediate classes, it may bedifficult to determine whether or not those valves 104 are defective. Tohelp reduce the number of probabilities falling into the intermediateclasses, the controller 108 uses both the process variable jumps 308 andthe output signal extreme positions 404. Also, different stictionpattern extraction techniques can be applied to the process variablejumps 308 and the OP signal extreme positions 404. In this way, thecontroller 108 more accurately classifies the stiction probabilities forthe defective and non-defective valves 104. As shown in FIG. 6C, whenusing both characteristics, the controller 108 correctly classifies theprobabilities for defective valves 104 and the probabilities fornon-defective valves 104 in most cases. Also, the controller 108 reducesthe number of probabilities falling within the intermediate classes.

The various classes 606-610 and 656-660 may be defined in any suitablemanner. For example, FIGS. 6A and 6B illustrate that the lower classes606, 656 range between zero and 0.2, the intermediate classes 608, 658range between 0.2 and 0.7, and the higher classes 610, 660 range between0.7 and 1.0. The classes could be defined using any other suitablecriteria.

Although FIGS. 6A through 6C illustrate one example of the probabilityclassifications indicating whether a valve 104 is defective, variouschanges may be made to FIGS. 6A through 6C. For example, the variousprobabilities and the classifications of those probabilities are forillustration only and depend on the particular valves 104 beingmonitored. Other probabilities and classifications could be produced. Asa particular example, depending on the noise and drift content in thedata, the controller 108 could more accurately classify theprobabilities as high or low and not in the intermediate range.

FIG. 7 illustrates an example method 700 for identifying a defectivevalve 104 according to one embodiment of this disclosure. For ease ofillustration and explanation, the method 700 is described with respectto the system 100 of FIG. 1. The method 700 could be used by any othersuitable system.

One or more characteristics associated with the operation of a valve 104are identified at step 702. This may include, for example, thecontroller 108 receiving and storing the process variable signal 112produced by the measuring device 106. This may also include thecontroller 108 storing the output signal 116 previously produced by thecontroller 108.

One or more of the identified characteristics are filtered or denoisedat step 704. This may include, for example, the controller 108 filteringthe process variable signal 112 and/or the output signal 116.

Possible indications of a valve defect are identified at step 706. Thismay include, for example, the controller 108 identifying one or moreprocess variable jumps 306 at one or multiple resolution levels. Thismay also include the controller 108 identifying one or more outputsignal extreme positions 404 at one or multiple resolution levels.

Patterns associated with the possible defect indicators are identifiedat step 708. This may include, for example, the controller 108 analyzingthe identified process variable jumps 306 and/or the output signalextreme positions 404. This may also include the controller 108generating one or more sets of stiction patterns 506 for each of theresolution levels.

One or more indexes are generated at step 710. This may include, forexample, the controller 108 generating one or more indexes for eachresolution level. An overall probability of a valve defect is generatedat step 712. This may include, for example, the controller 108 using theindexes produced at step 710 to identify the most accurate resolutionlevel. This may also include the controller 108 using the indexesassociated with the most accurate resolution level to generate theoverall probability of defect for the valve 104.

The overall probability of a defect is classified at step 714. This mayinclude, for example, the controller 108 determining whether the overallprobability falls into a high, intermediate, or low probability class.At this point, the controller 108 or the system 100 may take any othersuitable action. For example, when a high overall probability isdetected, the controller 108 could inform a technician that the valve104 needs servicing.

Although FIG. 7 illustrates one example of a method 700 for identifyinga defective valve 104, various changes may be made to FIG. 7. Forexample, the controller 108 need not denoise the characteristics at step704. Also, the controller 108 could perform adaptive denoising for thePV signal 112 and OP signal 116 at each resolution level between steps706 and 708. The denoised versions of the PV signal 112 and the OPsignal 116 are used for stiction pattern extraction and indexcalculation at each resolution level. As a particular example, thedenoising may be done differently for stiction pattern extraction andindex calculation at different resolution levels, such as when lessdenoising or filtering is done for higher resolution levels and moredenoising or filtering is done for lower resolution levels. In addition,the controller 108 could perform stiction pattern verification betweensteps 708 and 710, where falsely identified stiction patterns may beexcluded from the index calculation.

FIG. 8 illustrates an example method 800 for identifying possibleindications of a defective valve 104 according to one embodiment of thisdisclosure. For ease of illustration and explanation, the method 800 isdescribed with respect to the system 100 of FIG. 1. The method 800 couldbe used by any other suitable system.

A monitored characteristic is decomposed into multiple decomposition orresolution levels at step 802. This may include, for example, thecontroller 108 performing wavelet decomposition to decompose the processvariable signal 112 and produce wavelet coefficients at multipleresolution levels 302 a-302 g.

The decomposition or resolution levels are grouped into multiple groupsat step 804. This may include, for example, the controller 108 groupingthe wavelet coefficients in different resolution levels 302 a-302 g intotwo or more groups 304 a-304 e. In particular embodiments, the groups304 a-304 e overlap, where each group contains wavelet coefficients atthree adjacent resolution levels.

The groups of decomposition or resolution levels are used to identifyindications of a valve defect at step 806. This may include, forexample, the controller 108 performing singularity detection using thegroups 304 a-304 e to identify process variable jumps 306 at multipleresolution levels 308 a-308 e.

The following represents one technique for identifying PV jumps 306using singularity detection. In particular, the following describes theuse of the discrete dyadic wavelet transform. Other techniques couldalso be used. The controller 108 groups wavelet coefficients from afixed number of adjacent resolution levels 302 (in the example in FIG.3B, groups of three). The controller 108 then performs singularitydetection to detect a PV jump 306 using the group of waveletcoefficients.

As a particular example, assume that the controller 108 is attempting toidentify a PV jump 306 using wavelet coefficients from resolution levelsL, L-1, and L-2. The controller 108 may identify a PV jump 306 atresolution level L if all three of the following conditions are met:W _(—) PV(p,L) is a local maximum/minimum point;  Condition 10.7<W _(—) PV(p,L)/W _(—) PV(p,L-1)<2; and  Condition 20.6<W _(—) PV(p,L)/W _(—) PV(p,L-2)<4;  Condition 3where W_PV(p,L) represents the wavelet coefficient at position p inresolution level L. The values of 0.6, 0.7, 2, and 4 could be replacedby any suitable values. For example, 0.6 and 0.7 could be replaced byany values less than one, and two and four could be replaced by anyvalues greater than one, depending on the general characteristics of thevalve 104. In this example, the value W_PV(p,L) may represent a localmaximum/minimum point when either of the following conditions is met:W _(—) PV(p,L)>0 and W _(—) PV(p,L)>W _(—) PV(p−1,L) and W _(—)PV(p,L)>W _(—) PV(p+1,L); or  Condition 1aW _(—) PV(p,L)<0 and W _(—) PV(p,L)<W _(—) PV(p−1,L) and W _(—)PV(p,L)<W _(—) PV(p+1,L).  Condition 1b

This represents one possible technique for identifying PV jumps 306 atmultiple resolution levels. Various changes may be made to thistechnique. For example, any number of resolution levels could be groupedtogether. Also, the controller 108 could calculate an estimated noisevalue from the first resolution level and use this noise value as anadditional condition when deciding if the value W_PV(P,L) is a reallocal maximum/minimum point. As a particular example, the controller 108could determine whether the value W_PV(P,L) exceeds the noise threshold.If not, the value W_PV(P,L) is not a local maximum/minimum.

Although FIG. 8 illustrates one example of a method 800 for identifyingpossible indications of a defective valve 104, various changes may bemade to FIG. 8. For example, any other suitable technique could be usedto identify possible indications of a defective valve 104.

It may be advantageous to set forth definitions of certain words andphrases used throughout this patent document. The terms “include” and“comprise,” as well as derivatives thereof, mean inclusion withoutlimitation. The term “or” is inclusive, meaning and/or. The phrases“associated with” and “associated therewith,” as well as derivativesthereof, may mean to include, be included within, interconnect with,contain, be contained within, connect to or with, couple to or with, becommunicable with, cooperate with, interleave, juxtapose, be proximateto, be bound to or with, have, have a property of, or the like. The term“controller” means any device, system or part thereof that controls atleast one operation. A controller may be implemented in hardware,firmware, software, or some combination of at least two of the same. Thefunctionality associated with any particular controller may becentralized or distributed, whether locally or remotely.

While this disclosure has described certain embodiments and generallyassociated methods, alterations and permutations of these embodimentsand methods will be apparent to those skilled in the art. Accordingly,the above description of example embodiments does not define orconstrain this disclosure. Other changes, substitutions, and alterationsare also possible without departing from the spirit and scope of thisdisclosure, as defined by the following claims.

1. A method, comprising: identifying one or more operatingcharacteristics associated with a valve; identifying one or moreindicators of a possible defect in the valve at each of a plurality ofresolution levels using at least one of the one or more operatingcharacteristics; generating a plurality of indexes associated with theresolution levels, the indexes based on the one or more indicators andeach identifying a likelihood of a valve defect; selecting one of theplurality of resolution levels using at least one of the indexes; anddetermining an overall probability of a valve defect using at least oneof the indexes that is associated with the selected resolution level. 2.The method of claim 1, wherein: the one or more operatingcharacteristics comprise at least one of: (i) measurements of a processvariable associated with operation of the valve and (ii) valuesgenerated to control the operation of the valve; and the one or moreindicators comprise at least one of: (i) jumps in the process variablemeasurements and (ii) extreme positions in the generated control values.3. The method of claim 2, wherein: the process variable measurementscomprise measurements of a flow rate of one or more materials flowingthrough the valve; and the generated control values comprise values usedto adjust an opening of the valve and thereby adjust the flow rate. 4.The method of claim 2, wherein identifying the one or more indicators atthe plurality of resolution levels comprises: performing waveletdecomposition on the process variable measurements to generate waveletcoefficients at the plurality of resolution levels; grouping the waveletcoefficients at different resolution levels into groups; and identifyingthe jumps in the process variable measurements at the plurality ofresolution levels using the groups of wavelet coefficients.
 5. Themethod of claim 4, wherein identifying the one or more indicators at theplurality of resolution levels further comprises identifying the extremepositions in the generated control values using a number of jumps in theprocess variable measurements at each of the resolution levels.
 6. Themethod of claim 1, wherein generating the plurality of indexescomprises: using the one or more indicators to identify one or morestiction events at each of the resolution levels, each stiction eventcomprising a stick phase and a slip phase; and generating the pluralityof indexes using at least one of the one or more stiction events, thestick phase of the one or more stiction events, and the slip phase ofthe one or more stiction events.
 7. The method of claim 1, whereinselecting one of the resolution levels comprises selecting theresolution level having the indexes resulting in a highest likelihood ofa valve defect.
 8. The method of claim 1, wherein: generating theplurality of indexes comprises identifying multiple sets of one or morestiction events using different operating characteristics and generatingmultiple sets of indexes using the sets of stiction events; anddetermining the overall probability of a valve defect comprisesdetermining a plurality of probabilities associated with the sets ofindexes and using the plurality of probabilities to determine theoverall probability.
 9. The method of claim 1, wherein identifying theone or more indicators at one of the resolution levels comprisesidentifying one or more indicators at that resolution level using dataassociated with multiple resolution levels.
 10. The method of claim 1,further comprising: at least one of: storing, transmitting, anddisplaying the overall probability of the valve defect.
 11. The methodof claim 10, wherein the at least one of storing, transmitting, anddisplaying the overall probability of the valve defect comprises:classifying the overall probability of the valve defect into one of aplurality of classifications; and notifying a user if the overallprobability of the valve defect is classified into one of one or morespecified classifications.
 12. An apparatus, comprising: a memoryoperable to store one or more operating characteristics associated witha valve; and one or more processors collectively operable to: identifyone or more indicators of a possible defect in the valve at each of aplurality of resolution levels using at least one of the operatingcharacteristics; generate a plurality of indexes associated with theresolution levels, the indexes based on the one or more indicators andeach identifying a likelihood of a valve defect; select one of theplurality of resolution levels using at least one of the indexes; anddetermine an overall probability of a valve defect using at least one ofthe indexes that is associated with the selected resolution level. 13.The apparatus of claim 12, wherein: the one or more operatingcharacteristics comprise at least one of: (i) measurements of a processvariable associated with operation of the valve and (ii) valuesgenerated to control the operation of the valve; and the one or moreindicators comprise at least one of: (i) jumps in the process variablemeasurements and (ii) extreme positions in the generated control values.14. The apparatus of claim 13, wherein the one or more processors arecollectively operable to identify the one or more indicators at theplurality of resolution levels by: performing wavelet decomposition onthe process variable measurements to generate wavelet coefficients atthe plurality of resolution levels; grouping the wavelet coefficients atdifferent resolution levels into groups; identifying the jumps in theprocess variable measurements at the plurality of resolution levelsusing the groups of wavelet coefficients; and identifying the extremepositions in the generated control values using a number of jumps in theprocess variable measurements at each of the resolution levels.
 15. Theapparatus of claim 12, wherein the one or more processors arecollectively operable to generate the plurality of indexes by:identifying one or more stiction events at each of the resolution levelsusing the one or more indicators, each stiction event comprising a stickphase and a slip phase; and generating the indexes using at least one ofthe one or more stiction events, the stick phase of the one or morestiction events, and the slip phase of the one or more stiction events.16. The apparatus of claim 12, wherein the one or more processors arecollectively operable to select one of the resolution levels byselecting the resolution level having the indexes resulting in a highestlikelihood of a valve defect.
 17. The apparatus of claim 12, wherein:the one or more processors are collectively operable to generate theplurality of indexes by identifying multiple sets of one or morestiction events using different operating characteristics and generatingmultiple sets of indexes using the sets of stiction events; and the oneor more processors are collectively operable to determine the overallprobability of a valve defect by determining a plurality ofprobabilities associated with the sets of indexes and using theplurality of probabilities to determine the overall probability.
 18. Acomputer program embodied on a computer readable medium and operable tobe executed by a processor, the computer program comprising computerreadable program code for: identifying one or more indicators of apossible defect in a valve at each of a plurality of resolution levelsusing at least one of one or more operating characteristics associatedwith the valve; generating a plurality of indexes associated with theresolution levels, the indexes based on the one or more indicators andeach identifying a likelihood of a valve defect; selecting one of theplurality of resolution levels using at least one of the indexes; anddetermining an overall probability of a valve defect using at least oneof the indexes that is associated with the selected resolution level.19. The computer program of claim 18, wherein: the one or more operatingcharacteristics comprise at least one of: (i) measurements of a processvariable associated with operation of the valve and (ii) valuesgenerated to control the operation of the valve; and the one or moreindicators comprise at least one of: (i) jumps in the process variablemeasurements and (ii) extreme positions in the generated control values.20. The computer program of claim 19, wherein the computer readableprogram code for identifying the one or more indicators at the pluralityof resolution levels comprises computer readable program code for:performing wavelet decomposition on the process variable measurements togenerate wavelet coefficients at the plurality of resolution levels;grouping the wavelet coefficients at different resolution levels intogroups; identifying the jumps in the process variable measurements atthe plurality of resolution levels using the groups of waveletcoefficients; and identifying the extreme positions in the generatedcontrol values using a number of jumps in the process variablemeasurements at each of the resolution levels.
 21. The computer programof claim 18, wherein the computer readable program code for generatingthe plurality of indexes comprises computer readable program code for:identifying one or more stiction events at each of the resolution levelsusing the one or more indicators, each stiction event comprising a stickphase and a slip phase; and generating the indexes using at least one ofthe one or more stiction events, the stick phase of the one or morestiction events, and the slip phase of the one or more stiction events.22. The computer program of claim 18, wherein the computer readableprogram code for selecting one of the resolution levels comprisescomputer readable program code for selecting the resolution level havingthe indexes resulting in a highest likelihood of a valve defect.
 23. Thecomputer program of claim 18, wherein: the computer readable programcode for generating the plurality of indexes comprises computer readableprogram code for identifying multiple sets of one or more stictionevents using different operating characteristics and generating multiplesets of indexes using the sets of stiction events; and the computerreadable program code for determining the overall probability of a valvedefect comprises computer readable program code for determining aplurality of probabilities associated with the sets of indexes and usingthe plurality of probabilities to determine the overall probability. 24.The computer program of claim 18, further comprising computer readableprogram code for classifying the overall probability into one of aplurality of classifications.
 25. A system, comprising: a valve; ameasuring device operable to generate measurements of a process variableassociated with operation of the valve; a controller operable togenerate output values for adjusting the valve based on the processvariable measurements; and a defect detector operable to: identify oneor more indicators of a possible defect in the valve at each of aplurality of resolution levels using at least one of the processvariable measurements and the output values; generate a plurality ofindexes associated with the resolution levels, the indexes based on theone or more indicators and each identifying a likelihood of a valvedefect; select one of the plurality of resolution levels using at leastone of the indexes; and determine an overall probability of a valvedefect using at least one of the indexes that is associated with theselected resolution level.
 26. The system of claim 25, wherein thedefect detector forms part of the controller.
 27. A method, comprising:identifying one or more operating characteristics associated with avalve; identifying one or more indicators of a possible defect in thevalve using at least one of the one or more operating characteristics;identifying one or more stiction patterns using the one or moreindicators; generating one or more indexes associated with one or moreof the stiction patterns and each identifying a likelihood of a valvedefect; and determining an overall probability of a valve defect usingat least one of the one or more indexes.